Quantum genetic algorithm optimizes SVM for efficient human action recognition.
problem Efficiently extracting motion features for human skeleton dynamics.
method Quantum genetic algorithm optimization of SVM with joint angles and variance.
result Proposed approach outperforms conventional SVM by 2.3% accuracy.
Quantum circuit optimization speeds up financial derivatives pricing.
problem Efficiently pricing financial derivatives on quantum computers.
method Pretraining conditional parameterized circuits for state-dependent functions.
result Quantum circuit implementation of derivatives' payoff function is more efficient.
Optimized reverse quantum annealing speeds up portfolio optimization.
problem Optimizing portfolios using quantum and classical methods.
method Hybrid quantum-classical approach, including reverse quantum annealing.
result Optimized reverse quantum annealing is 100 times faster than forward quantum annealing.
Machine learning detects non-local correlations in complex scenarios.
problem Systematic derivation of non-local correlations becomes unfeasible in complex scenarios.
method Ensemble of multilayer perceptrons blended with genetic algorithms.
result High performance in relevant Bell scenarios.
Quantum computing aids in optimizing currency reserves for central banks.
problem Optimizing currency composition in foreign exchange reserves.
method Comparison of quantum and classical algorithms for portfolio optimization.
result Quantum algorithms outperform classical methods in currency optimization.
Hybrid classical-quantum framework optimizes portfolio rebalancing with reduced transaction costs.
problem Optimizing portfolio rebalancing with reduced transaction costs and lookahead bias.
method Combining Ledoit-Wolf shrinkage covariance estimation, hierarchical correlation clustering, entropy-regularised Genetic Algorithm, minimum-variance and equal-weight benchmarks, QUBO formulation, and QAOA for solving the combinatorial optimisation problem.
result GA + QAOA strategy outperforms classical methods with reduced rebalances and transaction costs.
GPO uses genetic algorithms for deep policy optimization in reinforcement learning.
problem Catastrophic consequence of parameter crossovers in neural networks for deep reinforcement learning.
method GPO uses imitation learning for policy crossover in the state space and applies policy gradient methods for mutation.
result GPO achieves superior performance and comparable sample efficiency compared to state-of-the-art policy gradient methods.
Introduces Deep Genetic Network for optimizing neural network hyperparameters.
problem Optimizing neural network hyperparameters is a tedious and time-consuming task.
method Uses genetic algorithms within a deep neural network architecture to optimize hyperparameters during training.
result Deep Genetic Network successfully optimizes hyperparameters in various types of neural network layers.
This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
Proposes new genetic algorithm rule for market competition.
problem Market competition genetic algorithm rules.
method Econophysics kinetic market model as an evolutionary algorithm.
result New replacement rule for genetic algorithms.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
problem Traditional methods for predicting stock market volatility have high errors.
method Back-propagation neural network and genetic algorithm integrated model.
result The model predicts future volatility with low errors and high accuracy.
Historical review of genetic algorithms for the TSP shows three distinct phases.
problem Optimizing routes for the Traveling Salesman Problem using genetic algorithms.
method Meta-data analysis of publications over time.
result Three distinct phases in the development of genetic algorithms for TSP identified.
A genetic algorithm improves multivariate kernel density estimation.
problem Efficiently estimating multivariate kernel density functions.
method Genetic algorithm applied to subsamples of the original data.
result The genetic algorithm-based estimator performs better than traditional methods.
Efficiently infers graph edges from genetic similarity data in landscape genetics.
problem Inferring unknown graph edges from genetic similarity data in a heterogeneous landscape.
method Developed an efficient first-order optimization method to solve the inverse landscape genetics problem.
result Our method provides fast and reliable convergence, significantly outperforming existing heuristics.
GADAM uses genetic algorithm to improve Adam's performance in deep learning.
problem Deep learning optimization stuck in local optima.
method GADAM combines Adam and genetic algorithm to evolve unit models.
result GADAM effectively avoids local optima and achieves faster convergence.
Hybrid ML and GA system improves currency exchange prediction.
problem Improving currency exchange prediction accuracy.
method Combines machine learning, genetic algorithms, and technical analysis.
result Improves return on investment from 0.43% to 10.29%.
Gradient GA uses gradient information to improve molecular design.
problem Random walk exploration limits genetic algorithms' quality and speed in molecular design.
method Gradient GA incorporates gradient information from the objective function into genetic algorithms, using a differentiable neural network and Discrete Langevin Proposal.
result Significantly improves convergence speed and solution quality over traditional genetic algorithms.
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
problem Improving sequential decision making tasks of online learning agents using multi-armed bandits.
method Integrates genetic principles into Thompson Sampling for multi-armed bandits.
result Significantly outperforms baselines in nonstationary settings.
Genetic algorithms optimize chess evaluation functions with mentor assistance.
problem Optimizing complex evaluation functions for superior performance.
method Genetic algorithms with mentor-assisted evolution.
result Programs evolved with mentors outperform top chess champions.
G2Ns combine genetic genes with neural networks for reinforcement learning.
problem Improving sample efficiency and performance in reinforcement learning.
method Genetic-Gated Networks (G2Ns) that integrate binary genetic genes with neural network hidden layers.
result G2Ns achieve significant improvements in sample efficiency and performance in reinforcement learning.
Genetic algorithms improve computer chess programs to grandmaster level.
problem Improving computer chess programs to match human grandmaster performance.
method Evolved a grandmaster-level evaluation function and search mechanism using genetic algorithms.
result The evolved program outperforms a world champion and matches other top programs.
Genetic algorithms optimize neural networks for cosmological data analysis.
problem Inaccurate results from neural networks due to poor hyperparameter selection.
method Used genetic algorithms to optimize hyperparameters of neural networks.
result Genetic algorithms improve neural network performance in cosmological data analysis.
New MCMC method for complex models with large variables.
problem Inference on posterior model probabilities in large model spaces.
method Reversible genetically modified mode jumping Markov chain Monte Carlo (GMJMCMC).
result Introduced a proper MCMC with correct limiting distribution.
We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which th…
GA improves deep neural networks by creating sparser models.
problem Training deep neural networks efficiently and sparsely.
method Genetic algorithms for evolving deep neural networks.
result GA-assisted deep autoencoder produces sparser neural networks.
Zoetrope Genetic Programming improves symbolic regression performance.
problem Evolutionary symbolic regression for complex mathematical expressions.
method Zoetropic representation, repeated fusion operations, linear combination, crossover, mutation, selection.
result Zoetrope Genetic Programming achieves state-of-the-art performance and low computational time.
New algorithm reconstructs genealogies from genetic data.
problem Reconstructing genealogies from genetic data.
method Iterative algorithm {\sc Rec-Gen} for pedigrees from a generative model.
result Accurate reconstruction of a large fraction of pedigrees with low sample complexity.
Expert-driven genetic algorithms improve chess evaluation functions.
problem Creating efficient evaluation functions for complex systems.
method Using genetic algorithms with expert guidance to evolve evaluation functions.
result Evolved programs outperform top chess programs with fewer parameters.
Genetic Algorithm improves Nelson-Siegel-Svensson model calibration for interest rates.
problem Calibrating the Nelson-Siegel-Svensson model is difficult due to nonlinearity and parameter co-dependence.
method Applied Genetic Algorithm to optimize model parameters.
result Constructs stable interest rate curves and model parameters over time.
Deep learning and genetic algorithms speed up cosmological Bayesian inference.
problem Substantial computational demands in Bayesian inference for cosmological parameter estimation.
method Deep learning using feedforward neural networks to approximate likelihood functions dynamically, optimized with genetic algorithms.
result Significant speed-up in Bayesian inference process for cosmological models and datasets.
A genetic algorithm with community detection improves feature selection accuracy.
problem Feature selection in high-dimensional datasets.
method Community detection in genetic algorithm for feature selection.
result The new approach yields improved classification accuracy.
New method designs multilayer nanoparticles using AI.
problem Difficult to design multilayer nanoparticles by trial and error.
method Combines genetic algorithm and neural network for inverse design.
result Successfully designs multilayer nanoparticles efficiently.
New IT representation improves symbolic regression approximations.
problem Finding better approximations to real-world data sets.
method Evolutionary Algorithm with IT representation using only mutation.
result IT representation finds better approximations than traditional methods.
GeNAS uses genetic algorithms to automatically assess human sperm morphology.
problem Manual sperm morphology analysis is inexact, subjective, and hard to teach.
method Genetic Neural Architecture Search (GeNAS) for automatic SMA.
result GeNAS achieved state-of-the-art results in sperm morphology detection.
GenSample uses genetic algorithms to improve minority class classification in imbalanced datasets.
problem Poor classification performance on minority class in imbalanced datasets.
method GenSample uses genetic algorithms to oversample minority class, considering difficulty and performance improvement.
result GenSample achieved the highest F-Score on 8 out of 9 real-world imbalanced datasets.
Hybridizes genetic algorithms with PROMOTHEE for better economic activity localization decisions.
problem Making optimal location decisions for economic activities considering multiple criteria.
method Hybridization of genetic algorithms with PROMOTHEE method for multicriteria analysis.
result Improves decision-making for economic activity localization.
Study compares neural nets and gradient boosting for traffic optimization, revealing accuracy issues near local optima.
problem Accuracy of neural nets and gradient boosting models in traffic optimization near local optima.
method 16 neural nets and 20 genetic algorithm settings analyzed for traffic optimization.
result Accuracy drops near local optima, affecting traffic optimization efficiency.
AI learns market manipulation through simulation, suggesting regulation.
problem Regulating AI to prevent market manipulation.
method Used a genetic algorithm in an artificial market simulation.
result AI discovered market manipulation as an optimal strategy.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
problem Inaccurate long-term stock price predictions.
method Adaptive Weighted Genetic Algorithm-Optimized SVR (IGA-SVR).
result Reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR.
A multilevel model combines genetic and imaging data for AD diagnosis.
problem Classification from multimodal genetic and brain imaging data with unbalanced contributions.
method Multilevel model with structured penalties for joint effects between modalities.
result The model reveals relationships between genes, brain regions, and disease status.
New algorithm detects interactions for better individual trait prediction.
problem Tackles the challenge of making accurate individual trait predictions using interactions.
method Extends model-based MDR (MB-MDR) to detect and utilize interactions.
result Outperforms other algorithms in detecting and utilizing interactions for better prediction.
BOSS optimizes string inputs using string kernels and genetic algorithms.
problem Optimizing string inputs with constraints.
method Bayesian optimization over string kernels and genetic algorithms.
result Significantly improved optimization across various string constraints.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
EvoNUDGE uses graph neural networks to improve genetic programming performance.
problem Efficiency in evolutionary computation for problem solving.
method Graph neural network to elicit additional knowledge from symbolic regression problems.
result EvoNUDGE significantly outperforms conventional and neural genetic programming methods.
Deep learning detects genetic interactions in type 2 diabetes.
problem Detecting genetic interactions in complex diseases like type 2 diabetes.
method Stacked Autoencoder for non-linear epistatic interactions.
result Deep learning can uncover missing heritability in complex diseases.
New models capture complex genetic causes of diseases.
problem Capturing causal relationships between genetic factors and diseases.
method Implicit causal models combining neural architectures and Bayesian inference.
result Significantly outperformed existing genetics methods.
A genetic algorithm-based method prunes deep neural networks for resource-constrained devices.
problem Resource constraints in deploying deep neural networks on embedded devices.
method Genetic algorithm for channel pruning of deep CNNs.
result Significant reduction in model size with minimal accuracy loss.
New computational methods improve clustering of objects.
problem Clustering objects based on their joint occurrence in sets.
method Genetic algorithm with renumbering, local search, and simplified model.
result Improvements enhance computational performance of genetic algorithm.